{"title":"多目标粒子滤波跟踪与自动事件分析","authors":"Yifan Zhou, J. Benois-Pineau, H. Nicolas","doi":"10.1145/1877868.1877876","DOIUrl":null,"url":null,"abstract":"The automatic video content analysis is an important step to provide the content-based video coding, indexing and retrieval. It is also a key issue to the event analysis in video surveillance. In this paper, an automatic event analysis approach is presented. It is based on our previous method of Multi-object Particle Filter Tracking with Dual Consistency Check. The multiple non-rigid objects are first tracked individually in parallel by multi-resolution technique and particle filter method. The events including object presence and occlusion identification are then detected and analyzed by measuring the Goodness-of-Fit Coefficient based on Schwartz's inequality and the Backward Projection. The method is then tested in different indoor and outdoor environments with cluttered background. The experimental results show the robustness and the effectiveness of the method.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Multi-object particle filter tracking with automatic event analysis\",\"authors\":\"Yifan Zhou, J. Benois-Pineau, H. Nicolas\",\"doi\":\"10.1145/1877868.1877876\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The automatic video content analysis is an important step to provide the content-based video coding, indexing and retrieval. It is also a key issue to the event analysis in video surveillance. In this paper, an automatic event analysis approach is presented. It is based on our previous method of Multi-object Particle Filter Tracking with Dual Consistency Check. The multiple non-rigid objects are first tracked individually in parallel by multi-resolution technique and particle filter method. The events including object presence and occlusion identification are then detected and analyzed by measuring the Goodness-of-Fit Coefficient based on Schwartz's inequality and the Backward Projection. The method is then tested in different indoor and outdoor environments with cluttered background. The experimental results show the robustness and the effectiveness of the method.\",\"PeriodicalId\":360789,\"journal\":{\"name\":\"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1877868.1877876\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1877868.1877876","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-object particle filter tracking with automatic event analysis
The automatic video content analysis is an important step to provide the content-based video coding, indexing and retrieval. It is also a key issue to the event analysis in video surveillance. In this paper, an automatic event analysis approach is presented. It is based on our previous method of Multi-object Particle Filter Tracking with Dual Consistency Check. The multiple non-rigid objects are first tracked individually in parallel by multi-resolution technique and particle filter method. The events including object presence and occlusion identification are then detected and analyzed by measuring the Goodness-of-Fit Coefficient based on Schwartz's inequality and the Backward Projection. The method is then tested in different indoor and outdoor environments with cluttered background. The experimental results show the robustness and the effectiveness of the method.